A unified structured factorization framework for quantum state tomography that parametrizes the density matrix as FF^dagger, supports multiple priors, provides sample complexity bounds, and introduces projected gradient descent and power-method algorithms.
Autoregressive neural network for simulating open quantum systems via a probabilistic formulation.Physical review letters, 128(9):090501, 2022
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Structured Factorization Approaches for Quantum State Tomography
A unified structured factorization framework for quantum state tomography that parametrizes the density matrix as FF^dagger, supports multiple priors, provides sample complexity bounds, and introduces projected gradient descent and power-method algorithms.